Joint denoising and HDR for RAW video sequences
This addresses the need for efficient high-dynamic-range video processing in computational photography, though it appears incremental as it builds on patch-based methods.
The paper tackles the problem of simultaneously denoising and fusing RAW multi-exposed video sequences, achieving state-of-the-art fusion results with real RAW data.
We propose a patch-based method for the simultaneous denoising and fusion of a sequence of RAW multi-exposed images. A spatio-temporal criterion is used to select similar patches along the sequence, and a weighted principal component analysis permits to both denoise and fuse the multi exposed data. The overall strategy permits to denoise and fuse the set of images without the need of recovering each denoised image in the multi-exposure set, leading to a very efficient procedure. Several experiments show that the proposed method permits to obtain state-of-the-art fusion results with real RAW data.